Threat Modeling for AI Systems: A Beginner's Security Guide

As artificial intelligence becomes deeply integrated into modern software, securing these systems against unique vulnerabilities is more critical than ever. Traditional security practices often fall short when addressing the unpredictable nature of machine learning models and data pipelines. This text-only course guides you through the foundational principles of threat modeling specifically tailored for AI systems. You will transition from understanding basic security concepts to systematically identifying, analyzing, and mitigating risks across the entire AI lifecycle, from data ingestion to model deployment. What you'll learn: 1. Understand foundational AI security terminology and the core mechanics of machine learning pipelines. 2. Identify unique AI vulnerabilities including prompt injection, data poisoning, and model evasion. 3. Apply structured threat modeling frameworks to intelligent applications. 4. Analyze the OWASP Top 10 for Large Language Models to recognize modern exploit vectors. 5. Design mitigation strategies to protect training data, model parameters, and user privacy. 6. Evaluate security risks in Retrieval-Augmented Generation (RAG) architectures. The curriculum begins with essential definitions of AI architectures and security baselines before moving into conceptual exercises. You will progress through step-by-step written explanations that demonstrate how to map data flows, pinpoint attack surfaces, and document security controls. This course is designed for aspiring security analysts, software engineers, and system architects who are new to AI security, requiring no prior experience in threat modeling. Start reading today to build a secure foundation for the future of artificial intelligence.